{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 机器学习100天——第25天：决策树（Decision Tree）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第1步：导入需要用到的python库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第2步：导入数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('../datasets/Social_Network_Ads.csv')\n",
    "X = dataset.iloc[:, [2, 3]].values\n",
    "y = dataset.iloc[:, 4].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第3步：将数据集拆分为训练集和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第4步：特征缩放"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ymao/usr/miniconda/lib/python3.6/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第5步：对测试集进行决策树分类拟合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=0,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第6步：预测测试集的结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第7步：制作混淆矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第8步：将训练集结果进行可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GYcTIlfOvZELzhJK2Cc0TuHJ+tPLXAFctuYpFr17Epic2seCFC/jO7d+J3GcxFlQ2DMOI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\nnsJsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0c9e6b37f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_train, y_train\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "plt.title('Decision Tree Classification (Training set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第9步：将测试集结果进行可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t5fCI40aZsysogURtF6m3OL2M/tnMzgOeBLqBf3T3O1JPWTOIqq6pdYqGtHrj\n1Bpoot5fThpjC6qYJuO4J2FLRFA47kk46TkLR2z7l8+v5oiSCQIPHwDWr4/1HSxb1cqSl48skRx+\nINhOmcAkkpYxA4KZfcrd3wvcEbFNklbrhHWQTm+cWgNN1Pur6aVUqyqqvD7xA3jzhYzKpD/xA/ir\nI3uA4SqZcl0Z407wt3j/ybByLUvPHR4stexOWDxwck0Bodw4ClUlSSVxqozOI+hlVOzlEdskCXme\noqHWQBP1/mnTcjeD6WvXt2Mr+0dn0uvbee2qs0ZUG5XryjhKuaDe2cniPlj8H8l9BpED1aocmCbN\nqdJsp28F/gboMrP7il7qAH6SdsKaVrNN0VCvsQXV6Opi8QPrWHx/Scmle3TJ5dpLu3j39euYfGB4\n33KzPtTc00skZZVKCP8N3A58Anhf0fbd7v7HVFPVzOo8RUPTqCbQxqgeK1TJrJoNay8YuWrd1ANw\n9L7Rh31kGnSlMH1DrKkrRGKoNNvpLmAXcDmAmc0EJgNTzWyqu2+qTxKbTB2naGgq1QbaCnftkRnw\n6cGveTC6YwDB0qRLz40+VS3LoIokKU6j8oXAZ4BjgR3AXGAt8MxaT25m5wOfBVqBa939k7Uec0JQ\nFULy6hhoJ122lkX3w8fughN2weZp8A8vhhV/XMiKVSU717oMqkiC4jQqfww4E/iBu59uZi8GXlvr\nic2sFfg3gkbrLcAvzey77v67Wo/dMLQkZH3FDLQvnreKNZ0+5n5R9hwGgy2w5eKFXHHx8PZVG3q4\nZX/PqP1/8x8wp7Qn7tAQmx5dy3NeWWZCwCKP/0tbvMkBRWKIExAOuvtOM2sxsxZ3v8vM/jWBc78A\neNjdewHM7EbgIqA5AoKWhMytVXOd1tbxz3i6YM7oDLq1tY09EWP8j38yekTy8U/CniPGTkPL0gGG\nSksdIuMU56/+CTObCvwIWGFmO4CnEjj3ccDmoudbgD8r3cnMlgBLAOZMpJ42SYw3kNTMj8jU0zje\njhmrI7ut7pjRzvw5Y88R1UjrNkj+xQkIFwH7gauAxQTDZT6SZqKKuftyYDnAGR0d4yvH51GexxtI\n3TLady2A5SuLpr4gaIB+14J+ZfZSd3GmrngKwMyOBFYmeO5HgROKnh8fbmsOzTbeoIEMfTZicqE0\ntfXDwP5gBTkzjmibzIoftrPihzHee1oKq+NI04rTy+jNwIcJSglDBGNuHKi1Y/wvgaeb2YkEgWAR\n8Jc1HrPS2RG2AAAP9klEQVRxaLxBftU7ky3uXHDYYTBvnqoNJRNxqozeDZzq7o8leWJ3HzCztwHf\nJ+h2ep27P5DkOXItzW6QUb2X0jqX1EadCyRH4gSE3wN70zi5u98G3JbGsRtCGuMNojKYtWvBLKiS\nKGxTplNZvboEq3OB5EicgPB+4Kdm9nPgUKW3u/9daqmS8YvKYGA4GBQo0ymvnnft6lwgORInIHwB\n+CFwP0EbguRZNRmJMp1oSdy1x622U+eCRPTt6dMSmAmIExAmufs7U0+JJKOalcmU6USr9a69mmq7\nWbPqtybEBNW3p491O9cx5MFn2D/Yz7qdQYlOQaE6cQLC7eHgsJWMrDLSjKd5VG5lsuLMCIYznVrr\nytevh61bh58feyycfHJt15C1Wu/aq6m227kTurvV4F+D3sd7DwWDgiEfovfxXgWEKsUJCJeHv99f\ntC2JbqeShnK9l8ptq6WuvDQYwPDzPAaFuMGv1i7B1VbbaTLDmvQPRn/e5bZLeXEGpp1Yj4RIgspl\nMKXbVq+ura68NBgUb89bQKimobjWLsGqtqur9tb2yMy/vVWfbbUqrZh2jrv/0MxeFfW6u38zvWRJ\nXaTZw2X16nxVgdSze2e11XZSk67pXSPaEABarIWu6fpsq1WphLCAoHfRhRGvOaCA0OhaW4cXgi/d\nXqtCUMnLmIdqgl+t3U6rqbbLOlBOAIV2AvUyql2lFdM+GD78iLtvKH4tnG5CGp1FrvxbfnupY48t\nX21ULA9jHqppKE6iNBG32k4S0Tm1UwEgAS0x9rk5YttNSSdEMjAQPRd/2e2lTj45CApxZD3moasr\nqKIpVq7KRoPFpElVakN4BsEymdNK2hGOJFhbWWqV9YppSQyKOvnkkQ3IhbaDWo6ZhjysVZ319y0y\nhkptCN3ABcBRjGxH2A28Kc1ENYU8TGqWxoyreZ7FNcvunX198OCDIwemPfjgcLpEcqBSG8J3gO+Y\n2VnuvrqOaWoOeZjULI275jzciZcT9w69XMmprW38vaceemj0wDT3YHsNn03Lgp54Oy4Y9ymkicQZ\nmHaJmT0A7AO+BzwbuMrdv5Zqyia6vNRTp3HXXM0x61WNUk2JLKqUYxa0rRTaV6ot0dXaXlPBghMX\n1nwMEYjXqPxSd3+SoProEeAk4O/TTFRTKFennlZde19fcHfb0xP87utL5zzVKGTSpV1U00hbpRJZ\nqc7OYDqJwnfR3j66QbrS+0UaVKzJ7cLffwF8w913WdxuiVJePeva89BeEaWe1WbVlshKSzk9PdW9\nv7TkUzoorSCJMR8iCYkTEFaa2YMEVUZvNbNjCJbTlFrUs649D+0VUepZbVbrILxqemRFBeByN1F1\nnOJj1Yaeup1LGlOcuYzeZ2afBna5+6CZ7QUuSj9pTaBevV7y0l5Rqp5rAdQ6CK+aEl1UAHYPGqVb\nWzNpbF+zfQ2tQ3Dwxwvrcj5pTGXbEMzsPUVPz3X3QQB3fwrQammNpN7tFXFVM1isVrU26ka1K3R3\nR2fo5QLtwACcdRYsXBj8zkPPK5EilUoIi4BPh4/fD3yj6LXzgQ+klShJWF7HBqRZbVZah1+uyqia\noBi3RJfnVdA0OE4qqBQQrMzjqOeSZ3keG5BGtVncOvy0gmJOA/Ci+8m8c0Ee2zEWnLhQS3CGKgUE\nL/M46rnkXTMtwpJ1HX4OAnBpBtdiLXzsLjLtXDD02aNSP0e1Jv3tE/x8y8/pH+zXEpxUDgjPMbMn\nCUoDU8LHhM81l5HkV6U6/Pnz65OGDANw1BrDAD+eA3Pvj3hDvToXnHZafc5TlR72D+zHS+5xm3UJ\nzkpTV6iDtDSmPNfh10HUGsMA7zsPXhsREDZOg5Ne1JN+wnJosIVRwaCgGZfgjDMOQaSx5LQOv17K\nZWSPdsDeSXD4weFteyfBR8+fwtQjJl6w7B/oP3T3bxiT2ybT3jb6Ovcd3KclOEMKCDLx5KAOP0tl\n1xhua+czr+/iypt7mbmznx0z2rn20i5+f3YneazMqUWh2qxw9+84/YP9zDtq3qhqoNIqNmjeJTgV\nEGRiaqZG9BKV1hi+84RO7jx74n8uUdVm5doFtATnsEwCgpm9GvgQcArwAne/J4t0iExESWVwjdwV\ns1y1WbntWoIzkFUJ4bfAq4AvZHR+GQ8NamoYtWZwUT2VGqkrZtlqsyZsF6hGJgHB3dcCaNbUBlLP\nGVMVeDJXTZVLNepV6qhUbSblqQ1BRovKkOs1Y2pep+qewKIy6WqrXOKep16lDrULjE9qAcHMfgDM\ninhpabg8Z9zjLAGWAMxpkn7kmSqXIZcGg4KkBzXldaruHKvlrrtcJt1qrQz66LmfaqlySavUUY7a\nBaqXWkBw95ckdJzlwHKAMzo6NGVG2splyOUkHaTzOlV3TjnUdNddLpNua2mjhZZEq1zSKHVIsuIs\noSnNpFLGW9rmY5b8YK+8TtWdY+XuuuMolxkPDA3QPaP7UImgvbWd7hndNd1xlytdqKE3P7LqdnoJ\n8DngGOBWM1vj7i/LIi1Soty0D21to9cOiFoSslZNPso4KXHvuiv1xkm6ykUNvfmXVS+jbwHfyuLc\nTStuz51yGXK5zD/puv0mH2WclLh33ZUy6aR7BKmhN//Uy6gZVNNzp1yGvHZt9LHTqNtv4lHG49Fi\n46/rL5dJQ21tE5XOpwCQXwoIeZR0P/xqe+5EZciF9JRS3X6mDOie0V3TXXdUJr168+q69giSfFBA\nyJs0+uEn0XOn0er2m2hwWxp33eoR1JzUyyhvKt3Nj1cSPXeqWWQ+a4WgWgh4haDa15dtuhqIegQ1\nJ5UQ8iaNfvhJ3d03St2+BrfVTD2CmpMCQt6ksdpXs/Xc0eC2mqlHUHNSQMibtOrqG+XuPgmVxlKs\nXj3hgmJaE8apR1DzUUDIm2a7m09DVFA1CwbWFQbXTZBJ88pNXfHIE4+MWi7ytFkTbV00SZoCQh6l\ncTefVq+bPPbmiQqqAwMwWDJZ2wRpV4jqHrrv4D4O9u8bsX3Vhh4WnLiwjimTRqOA0AzSmlI6z1NV\nlwbVnp7o/SZou4IBB3+8cHjDmjW0vP2JrJIjDUIBoRmk1eum3r158lgayak5/eoeKtVTQGgGafW6\nqWdvnjyXRjJWOnUFDst61T1UqqeBac0grSml6zlVda0D9ibotNqFqSuKp6kGWLyjuYOkjI9KCM0g\nra6saR03qmqo1tJIo029UYXS7qGrNvRklxhpaAoIzSCtrqxpHLdc1VDUegwQ/w5f3XlFxqSA0CzS\nGpiW9HHLVQ2ZBXf0tdzhN9PgPJFxUBuC5Eu5KqDBwcaZXE+kQamEIPlSaS4n3eGLpEolBMmXrq6g\nKqjYBGn8Fck7lRAkX9T4K5IZBQTJH1UNiWRCVUYiIgIoIIiISEgBQUREAAUEEREJKSCIiAiggCAi\nIiEFBBERATIKCGb2T2b2oJndZ2bfMrOjskiHiIgMy6qEcAdwqrs/G1gPvD+jdIiISCiTgODu/+vu\nhcntfwYcn0U6RERkWB7aEN4A3F7uRTNbYmb3mNk9fzh4sI7JEhFpLqnNZWRmPwBmRby01N2/E+6z\nFBgAVpQ7jrsvB5YDnNHR4SkkVURESDEguPtLKr1uZlcAFwDnursyehGRjGUy26mZnQ+8B1jg7nuz\nSIOIiIyUVRvC54EO4A4zW2Nm/5lROkREJJRJCcHdT8rivCIiUl4eehmJiEgOKCCIiAiggCAiIiEF\nBBERARQQREQkpIAgIiKAAoKIiIQUEEREBFBAEBGRkAKCiIgACggiIhJSQBAREUABQUREQgoIIiIC\nKCCIiEhIAUFERACwRlrO2Mx2A+uyTkcKjgYeyzoRKZio1wUT99om6nXBxL22ONc1192PGetAmayY\nVoN17n5G1olImpndo+tqLBP12ibqdcHEvbYkr0tVRiIiAiggiIhIqNECwvKsE5ASXVfjmajXNlGv\nCybutSV2XQ3VqCwiIulptBKCiIikRAFBRESABgsIZvZRM7vPzNaY2f+a2bFZpykpZvZPZvZgeH3f\nMrOjsk5TEszs1Wb2gJkNmVnDd/kzs/PNbJ2ZPWxm78s6PUkxs+vMbIeZ/TbrtCTJzE4ws7vM7Hfh\n3+Hbs05TUsxsspn9wsx+E17bh2s+ZiO1IZjZke7+ZPj474A/dfe3ZJysRJjZS4EfuvuAmX0KwN3f\nm3GyamZmpwBDwBeAd7v7PRknadzMrBVYD5wHbAF+CVzu7r/LNGEJMLM/B/YAX3X3U7NOT1LMbDYw\n293vNbMO4FfAxRPkOzPgCHffY2aTgLuBt7v7z8Z7zIYqIRSCQegIoHGi2Rjc/X/dfSB8+jPg+CzT\nkxR3X+vuE2V0+QuAh929190PADcCF2WcpkS4+4+AP2adjqS5+zZ3vzd8vBtYCxyXbaqS4YE94dNJ\n4U9NeWJDBQQAM1tmZpuBxcA/Zp2elLwBuD3rRMgoxwGbi55vYYJkLs3AzOYBpwM/zzYlyTGzVjNb\nA+wA7nD3mq4tdwHBzH5gZr+N+LkIwN2XuvsJwArgbdmmtjpjXVu4z1JggOD6GkKc6xLJkplNBW4G\n3lFS09DQ3H3Q3U8jqFF4gZnVVN2Xu7mM3P0lMXddAdwGfDDF5CRqrGszsyuAC4BzvYEad6r4zhrd\no8AJRc+PD7dJjoX16zcDK9z9m1mnJw3u/oSZ3QWcD4y7Y0DuSgiVmNnTi55eBDyYVVqSZmbnA+8B\nXunue7NOj0T6JfB0MzvRzA4DFgHfzThNUkHY8PolYK27fybr9CTJzI4p9EY0sykEnR1qyhMbrZfR\nzUA3Qa+VjcBb3H1C3KGZ2cNAO7Az3PSzidCDyswuAT4HHAM8Aaxx95dlm6rxM7NXAP8KtALXufuy\njJOUCDO7AVhIMJVyH/BBd/9SpolKgJnNB34M3E+QbwB8wN1vyy5VyTCzZwNfIfhbbAG+7u4fqemY\njRQQREQkPQ1VZSQiIulRQBAREUABQUREQgoIIiICKCCIiEhIAUEkJjO72MzczJ6RdVpE0qCAIBLf\n5QQzSl6edUJE0qCAIBJDOBfOfOCNBCOUMbMWM/v3cB2LO8zsNjO7LHzteWa2ysx+ZWbfD6dhFsk1\nBQSReC4Cvufu64GdZvY84FXAPOBPgdcBZ8GhuXM+B1zm7s8DrgMmxIhmmdhyN7mdSE5dDnw2fHxj\n+LwN+Ia7DwHbw8nFIJhe5VTgjmAqHVqBbfVNrkj1FBBExmBmTwPOAZ5lZk6QwTvwrXJvAR5w97Pq\nlESRRKjKSGRslwH/5e5z3X1euB7HBoIVxi4N2xI6CSaHA1gHHGNmh6qQzOyZWSRcpBoKCCJju5zR\npYGbgVkEq6b9DvgacC+wK1xe8zLgU2b2G2ANcHb9kisyPprtVKQGZjY1XOR8BvAL4IXuvj3rdImM\nh9oQRGpzS7hIyWHARxUMpJGphCAiIoDaEEREJKSAICIigAKCiIiEFBBERARQQBARkdD/B+hKtRzz\nifLpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0c9d686e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_test, y_test\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "plt.title('Decision Tree Classification (Test set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>完整的项目请前往Github项目<a href=\"https://github.com/MachineLearning100/100-Days-Of-ML-Code\">100-Days-Of-ML-Code</a>查看。有任何的建议或者意见欢迎在issue中提出~</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
